Systems and methods for dynamically generating optimal routes for vehicle operation management

A computer-implemented method for generating a respective optimal route for each vehicle of a plurality of vehicles, the method implemented using a vehicle routing analytics (VRA) computing device communicatively coupled to the plurality of vehicles, wherein the VRA computing device includes at least one processor in communication with at least one memory, wherein the method comprises:

• retrieving a vehicle definition for each vehicle of the plurality of vehicles, each vehicle definition including availability parameters and delivery preferences associated with the respective vehicle;
• retrieving a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and priority criteria;
• executing at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions as inputs;
• receiving, as output from the executed at least one of artificial intelligence and deep learning functionality, a respective subset of tasks for each vehicle of the plurality of vehicles;
• generating a respective optimal route for each vehicle of the plurality of vehicles, each optimal route including a scheduled list of the subset of tasks for the vehicle to perform, wherein each optimal route prioritizes the subset of tasks based at least in part upon the priority criteria of the subset of tasks;
• and
• controlling at least one vehicle of the fleet of vehicles to travel along the optimal route.

At least one non-transitory computer-readable storage medium having stored thereon computer-executable instructions that, when executed by at least one processor of a vehicle routing and analytics (VRA) computing device communicatively coupled to a plurality of vehicles, cause the at least one processor to:

• retrieve a vehicle definition for each vehicle of the plurality of vehicles, each vehicle definition including availability parameters and delivery preferences associated with the respective vehicle;
• retrieve a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and priority criteria;
• execute at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions as inputs;
• receive, as output from the executed at least one of artificial intelligence and deep learning functionality, a respective subset of tasks for each vehicle of the plurality of vehicles;
• generate a respective optimal route for each vehicle of the plurality of vehicles, each optimal route including a scheduled list of the subset of tasks for the vehicle to perform, wherein each optimal route prioritizes the subset of tasks based at least in part upon the priority criteria of the subset of tasks;
• and
• control at least one vehicle of the plurality of vehicles to travel along the respective optimal route.